code: def test_model_trainable_and_decodable(module, model_dict):
      args = make_arg(**model_dict)
      if "pytorch" in module:
          batch = prepare_inputs("pytorch")
      else:
          raise NotImplementedError
  
      m = importlib.import_module(module)
      model = m.E2E(40, 5, args)
      loss = model(*batch)
      if isinstance(loss, tuple):
          # chainer return several values as tuple
          loss[0].backward()  # trainable
      else:
          loss.backward()  # trainable
  
      with torch.no_grad(), chainer.no_backprop_mode():
          in_data = np.random.randn(10, 40)
          model.translate(in_data, args, args.char_list)  # decodable
          if "pytorch" in module:
              batch_in_data = [
                  np.random.randn(10, 40),
                  np.random.randn(5, 40),
              ]
              model.translate_batch(batch_in_data, args, args.char_list)
  
imports: from __future__ import division
  import argparse
  import importlib
  import os
  import tempfile
  from test.utils_test import make_dummy_json_st
  import chainer
  import numpy
  import pytest
  import torch
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.utils.training.batchfy import make_batchset
label_desc: Use image node_id 4 for calling a global function with example usage: test_model_trainable_and_decodable(module, model_dict) without return types